The present invention relates generally to diagnostics of railroad locomotives and other self-powered transportation equipment, and, more specifically, to system and method for hybrid processing of quantized operational parameter data and fault log data to facilitate automated analysis of machine equipment undergoing diagnostics.
A machine, such as a locomotive or other complex systems used in industrial processes, medical imaging, telecommunications, aerospace applications, power generation, etc., includes elaborate controls and sensors that generate faults when anomalous operating conditions of the machine are encountered. Typically, a field engineer will look at a fault log and determine whether a repair is necessary.
Approaches like neural networks, decision trees, etc., have been employed to learn over input data to provide prediction, classification, and function approximation capabilities in the context of diagnostics. Often, such approaches have required structured and relatively static and complete input data sets for learning, and have produced models that resist real-world interpretation.
Another approach, Case Based Reasoning (CBR), is based on the observation that experiential knowledge (memory of past experiences or cases) is applicable to problem solving as learning rules or behaviors. CBR relies on relatively few pre-processing of raw knowledge, focusing instead on indexing, retrieval, reuse, and archival of cases. In the diagnostic context, a case generally refers to a problem/solution description pair that represents a diagnosis of a problem and an appropriate repair. CBR assumes cases described by a fixed, known number of descriptive attributes. Conventional CBR systems assume a corpus of fully valid or “gold standard” cases that new incoming cases can be matched against.
U.S. Pat. No. 5,463,768 discloses an approach which uses error log data and assumes predefined cases with each case associating an input error log to a verified, unique diagnosis of a problem. In particular, a plurality of historical error logs are grouped into case sets of common malfunctions. From the group of case sets, common patterns, i.e., consecutive rows or strings of data, are labeled as a block. Blocks are used to characterize fault contribution for new error logs that are received in a diagnostic unit. Unfortunately, for a continuous fault code stream where any or all possible fault codes may occur from zero to any finite number of times and where the fault codes may occur in any order, predefining the structure of a case is nearly impossible.
U.S. patent application Ser. No. 09/285,612, assigned to the same assignee of the present invention, discloses system and method for processing historical repair data and fault log data, which is not restricted to sequential occurrences of fault log entries and which provides weighted repair and distinct fault cluster combinations, to facilitate analysis of new fault log data from a malfunctioning machine. Further, U.S. Pat. No. 6,343,236, assigned to the same assignee of the present invention, discloses system and method for analyzing new fault log data from a malfunctioning machine in which the system and method are not restricted to sequential occurrences of fault log entries, and wherein the system and method predict one or more repair actions using predetermined weighted repair and distinct fault cluster combinations. Additionally, U.S. Pat. No. 6,336,065, assigned to the same assignee of the present invention, provides system and method that uses snapshot observations of operational parameters from the machine in combination with the fault log data in order to further enhance the predictive accuracy of the diagnostic algorithms used therein. That invention further provides noise reduction filters, to substantially eliminate undesirable noise, e.g., unreliable or useless information that may be present in the fault log data and/or the operational parameter data. This noise reduction allows increasing the probability of early detection of actual incipient failures in the machine, as well as decreasing the probability of falsely declaring non-existent failures.
U.S. patent application Ser. No. 09/688,105, assigned in common to the assignee of the present invention, provides process and system that uses anomaly definitions based on continuous parameters to generate diagnostics and repair data. The anomaly definitions in this case are different from faults in the sense that the information can be taken in a wider time window, whereas faults, or even fault data combined with snapshot data, are generally based on generally discrete behavior occurring at one instance in time. The anomaly definitions, however, may be analogized to virtual faults and thus, such anomaly definitions can be learned using the same diagnostics algorithms that can be used for processing fault log data.
It is believed that the inventions disclosed in the foregoing patent applications or patents provide substantial advantages and advancements in the art of computerized diagnostics. It would be desirable, however, to provide system and method that allows a field or diagnostic engineer or any other personnel involved in maintaining and/or servicing the machine to systematically analyze the fault log data together with quantized operational parameter data so as to identify respective indications and/or respective combinations of indications that otherwise could be missed. It will be shown that fault log data enhanced with quantized operational parameter data provides useful information for even more reliable and accurate detection of incipient failures. For example, it would be desirable to even more accurately identify any such anomalies and/or combinations so that such maintenance and/or service personnel is able to proactively make repair recommendations and thus avoid loss of good will with clients as well as costly delays that could result in the event of a mission failure of the machine. An example of a mission failure would be a failed locomotive unable to deliver cargo to its destination and possibly causing traffic gridlock in a given railtrack. It would be further desirable to identify data buckets indicative of respective levels of quantization for each operational parameter. It would be also desirable to configure the data buckets to capture and distinguish statistically-measurable influences on the performance of a given piece of equipment based on the quantization level of each respective operational parameter. This would quickly allow service personnel to compare any new fault log data together with quantized operational parameter data, as may be downloaded from the machine, with prior fault log data of the same machine so as to be able to issue even more accurate and reliable repair recommendations to the entity responsible for operating the locomotive.